Gender: Male 
Age: 55-60
Nationality: United States
Ethnicity: Asian / Pacific Islander 
Marital status: Married 
First language: English 
Religion: Other, please specify. Hindu
Education: Undergraduate degree
Mother's education: High school or below
Father's education: High school or below
Self-assessed socioeconomic status (1-10): 7
Home: Own 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Homemaker
Father's occupation: Elementary occupations (jobs that involve simple and routine tasks which may require the use of hand-held tools and considerable physical effort)
Hobbies: Use social media;Watch sports 
Devices used daily: Laptop;Smartphone;Tablet;Smartwatch 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
NLP tools they would find useful: Grammar checker (i.e., correcting grammar mistakes);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language) 
Frequency of LLM use: Every day
LLMs used: ChatGPT 
LLM use cases: Coding;Writing (e.g., composing emails);Proofreading/Editing (e.g., grammar, spelling, structure improvements);Analyze data;Completing assignments;Learning (e.g., explaining complex concepts in simple terms);Answering questions about general knowledge;Summarizing long texts 
Contexts of LLM use: Work;Personal 
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